Adversarial Learning of Poisson Factorisation Model for Gauging Brand Sentiment in User Reviews
This work addresses brand sentiment analysis for e-commerce platforms, offering a more nuanced approach than discrete sentiment categories, though it is incremental in its method.
The paper tackles the problem of detecting brand-associated sentiment in product reviews by proposing the Brand-Topic Model (BTM), which infers real-valued sentiment scores and fine-grained topics, outperforming baselines in brand ranking and topic quality on an Amazon dataset.
In this paper, we propose the Brand-Topic Model (BTM) which aims to detect brand-associated polarity-bearing topics from product reviews. Different from existing models for sentiment-topic extraction which assume topics are grouped under discrete sentiment categories such as `positive', `negative' and `neural', BTM is able to automatically infer real-valued brand-associated sentiment scores and generate fine-grained sentiment-topics in which we can observe continuous changes of words under a certain topic (e.g., `shaver' or `cream') while its associated sentiment gradually varies from negative to positive. BTM is built on the Poisson factorisation model with the incorporation of adversarial learning. It has been evaluated on a dataset constructed from Amazon reviews. Experimental results show that BTM outperforms a number of competitive baselines in brand ranking, achieving a better balance of topic coherence and uniqueness, and extracting better-separated polarity-bearing topics.